Statistical Pattern Recognition (Google eBook)

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John Wiley & Sons, Jul 25, 2003 - Mathematics - 514 pages
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Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.


Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems.


* Provides a self-contained introduction to statistical pattern recognition.
* Each technique described is illustrated by real examples.
* Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification.
* Each section concludes with a description of the applications that have been addressed and with further developments of the theory.
* Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability.
* Features a variety of exercises, from 'open-book' questions to more lengthy projects.


The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.

For further information on the techniques and applications discussed in this book please visitwww.statistical-pattern-recognition.net

  

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Contents

1 Introduction to statistical pattern recognition
1
2 Density estimation parametric
33
3 Density estimation nonparametric
81
4 Linear discriminant analysis
123
5 Nonlinear discriminant analysis kernel methods
169
6 Nonlinear discriminant analysis projection methods
203
7 Treebased methods
225
8 Performance
251
11 Additional topics
409
A Measures of dissimilarity
419
B Parameter estimation
431
C Linear algebra
437
D Data
443
E Probability theory
449
References
459
Index
491

9 Feature selection and extraction
305
10 Clustering
361

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About the author (2003)

Mathematicians and Calculus Students.

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